APISENSE - ISM 2013
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Crowdsensing uses mobile devices to collect data about human activity and behavior. This document discusses using crowdsensing to better understand crowd behavior and optimize services. It outlines the various sensors on mobile devices that can be used and challenges like privacy, energy use, and recruiting participants. A multi-cloud platform is proposed to address issues of scalability, privacy, and flexibility through a central node that guarantees anonymity and checks tasks.
APISENSE - ISM 2013
- 5. Challenges)summary 5 Task description Task execution Worker recruitment Task deployment Data)upload Worker rewarding Crosscutting challenges Privacy Energy Software challenges Scalability SecurityFlexibility Hot:topics Motivation Incentives www.apisense.fr
- 9. GSM$Signal$Open$Map 9 trace.setHeader(’gsm_operator’, gsm.operator()); location.onLocationChanged({ period: ’5min’, distance: ’10m’ }, function (loc) { return trace.add({ time: loc.timestamp, lat: loc.latitude, lon: loc.longitude, wifi: { network_id: wifi.bssid(), signal_strength: wifi.rssi() }, gsm: { cell_id: gsm.cellId(), signal_strength: gsm.dbm() } }); }); Ground truth Open map
- 10. Collecting*exceptions*in*the*wild 10 logcat.onLog {filter: ’*:E’}, (log) -> if log.message contains ’Exception’ trace.save message: log.message, time: log.timestamp, application: apps.process(log.pid).applicationName, topTask: apps.topTask().applicationName www.apisense.fr